Rutgers Cancer Institute of New Jersey
195 Little Albany Street
New Brunswick, NJ 08903-2681
The Center holds weekly meetings on Wednesdays (2:00 pm — 3:30 pm, Conference Room 3510) where PIs and trainees discuss ongoing projects in an interactive manner. The Center also often hosts speakers from the New York metropolitan area, who present their research in these meetings. Students interested in working with labs at the Center are strongly encouraged to attend these meetings.
Rutgers Cancer Institute’s Center for Systems and Computational Biology (CSCB) hosted a workshop from September 7 to 16, 2016, with the goal of introducing students of quantitative and biological sciences to important challenges in the field of cancer genomics.
Recent development of high-throughput experimental technologies and high-performance computing platforms have led to the creation of large public repositories of cancer datasets, demanding new quantitative methodologies to integrate and interpret large numbers of biological measurements. Understanding complexity, dynamics, and stochastic patterns in biological data, concepts native to quantitative sciences, are critical for elucidating how diseases like cancer originate and evolve. As researchers begin searching for more precision-oriented treatments in the coming years, computational methods will have important roles in dissecting the cellular and molecular heterogeneity that enables cancer to resist current treatments. These challenges present opportunities for trainees in quantitative sciences who can apply their perspectives to the study of disease.
In 14 lectures, we discussed fundamentals in experimental design and high-throughput techniques that systematically measure biological and genomic data from malignant tissue and during tumor evolution. We also introduced recent developments in mathematics to offer novel approaches for analyzing biological datasets, and described large public databases that contain the genomes of human diseases as well as disease-causing organisms. Our goal was to establish a multidisciplinary environment targeted to the strengths and interests of quantitative scientists and provide relevant problems for them to solve.
The workshop was open to all students and postdoctoral researchers, and approximately 40 attendees followed the lectures in the class or on the web.
Experimental design and power of data (Chang Chan)
High-throughput genome sequencing (Hossein Khiabanian)
Disease classification in cancer (Shridar Ganesan)
Introduction to genomic data analysis (Hossein Khiabanian)
Cancer imaging data (Nrusingh Biswal)
RNA-sequencing data analysis (Subho De)
Public cancer databases and genomic analysis tools (Saurabh Laddha and Hossein Khiabanian)
Clustering approaches for genomic data analysis (Gyan Bhanot)
Cancer pathway analysis (Michael Gatza)
Proteomics of cancer (Justin Drake)
High dimensional single-cell analysis (Pablo Camara, Columbia University)
Mathematical modeling of cancer evolution (Jiguang Wang, Columbia University)
Genomic translocation inference (Sakellarios Zairis, Columbia University)